ISSN 2756-3251
Advanced Journal of Environmental Science and Technology ISSN 2756-3251, Vol. 15 (7), pp. 001-007, July, 2024. Available online at www.internationalscholarsjournals.org © International Scholars Journals
Full Length Research Paper
Predicting Meteorological Trends in Makurdi Using Artificial Neural Network Approach
Chukwu, S. C.1*, and Nwachukwu, A. N.2
1Renaissance University, Ugbawka, Enugu, Nigeria.
2University of Manchester, Manchester, United Kingdom.
Accepted 21 February, 2024
The mean daily data for sunshine hours, maximum temperature, cloud cover and relative humidity data, were used to estimate monthly average global solar irradiation on a horizontal surface for Makurdi, Nigeria. The study used artificial neural networks (ANN) for the estimation. Results showed good agreement between the predicted and measured values of global solar irradiation. A correlation coefficient of 0.9982 was obtained with a maximum percentage error (MPE) of 0.8512 and root mean square error (RMSE) of 0.0032. The comparison between the ANN and some existing empirical models showed the advantage of the ANN prediction model.
Key words: Sunshine hours, relative humidity, maximum temperature, cloudiness index, global solar radiation.